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The overall objective of this study was to address two main issues that explain how

accessibility to microcredit, including formal and informal microcredit sectors and to what extent formal microcredit has impacts on rural households. There are four research objectives: (i) to review the Vietnam rural credit market and microcredit programmes targeting the rural poor household; (ii) to identify factors affecting microcredit accessibility, both loan amount of formal and informal microcredit; (iii) to evaluate the impact of the microcredit programme on rural household, including impact on income and consumption; and (iv) to identify policy implications towards improving microcredit accessibility and impact to rural households through improving government policies towards the rural credit market and rural household.

Chapter 2 provides an overview of the Vietnam rural credit market and microcredit programmes targeting rural poor households. The rural credit market in Vietnam is

characterised as a segmented and emerging market wherein the growing demand for credit by poor and low-income households is unmet. There are three types of credit providers: formal, informal and semi-formal. The formal credit sector was driven by a series of institutional changes and credit policies designed to cover the credit demand of rural households, particularly the rural poor. An increasing number of loans in formal microcredit have been recorded but a large proportion of the poor are still unable to borrow from the formal microcredit sector hence seek an alternative source of credit. The informal credit sector, traditionally an alternative source to formal credit, is prevalent as the alternative for many rural households; it exists alongside the formal credit sector. The semi-formal microcredit sector, dominated by NGOs and donor support funds, began in the market in late 1990s, and has an increasingly important role in providing microcredit and microfinance services to the

181 poor but on a small scale. Limited access to formal credit not only constrains the rural

households from expanding their production scales but also prevents farmers from improving their living conditions. Credit inaccessibility in the rural areas thus impedes the development of the rural sector, which potentially decelerates the development of the Vietnam rural economy.

Chapter 3 reviews the literature on accessibility to microcredit and microcredit programme impact on rural households. The review of literature on microcredit for both theoretical and empirical models suggests that asymmetric information (i.e., transaction costs) plays an important role in accessibility to credit and hence imposes credit rationing on borrowers. The government microcredit programme is designed to provide more microcredit access to the rural borrowers with collateral free using the third party‟s screening (i.e., the Peoples Committee of the village), but credit rationing still persists in lending due to third party‟s incentives (e.g., commission and responsibility to deal with default loans). Rural households, particularly the poor and landless households still have no access to many microcredit

programmes; therefore, they seek alternative sources of credit. Informal microcredit relies on simple and flexible lending practices supplies credit to a large proportion of rural borrowers. The informal credit sector exists in the rural credit market; however, the literature shows a gap that there is little focus on how these two credit sectors interact to serve a wider range of rural borrowers. The gap is closely attributed to modelling and estimating issues. On one hand, including informal credit in the formal credit equation is not feasible because in the formal lending practice, loans are not given to a borrower based on the household‟s informal debt. On the other hand, specifying separate borrowing and loan amounts for both sectors in the model generates a multi-distributional problem in estimation.

In addition, the review focusing on the impact of formal microcredit programmes on rural households demonstrates microcredit programme impact on households remains questionable due to contradictory results from empirical studies. In fact, the results of microcredit

programme impact studies are subject to biases because of missing data problem. In general,

impact evaluation methods such as Matching or DinD deal with either observed or

unobserved biases. Provided there is non-experimental data in the microcredit survey, no single method can entirely overcome bias; a combined method, therefore, can improve unbiased estimators of the impact evaluation.

182 Chapter 4 discusses a number of important issues in microcredit literature including

asymmetric information, credit constraint and credit rationing, and consumption and established the empirical models for the research objectives 2 and 3. Various economic relationships have been established for empirical estimation in the theoretical models. Information asymmetry including transaction cost is the core of the lending principle that explains why lenders always select a certain borrower to grant a loan contract and hence there is always a mismatch between credit supply and demand in the formal rural credit market. This credit rationing creates credit constraints on the rural household, particularly the rural poor. Subsequently, the household consumption model shows that credit helps reduce constraints to working capital in agricultural production or in non-farm income generating

activities, hence, enhances the household‟s consumption growth. Following the defined

economic relationship, different empirical models were discussed. First, the credit

accessibility model was specified to determine factors affecting the household‟s decision to borrow from the formal credit sector under the conditions that the informal sector exists and interacts with the formal sector in the credit market. The model is expected to achieve consistent estimators for the determinants of the household‟s access to the formal and informal credit under the credit rationing assumption, selection bias and interaction between

the informal and formal credit. Next, the PSM method and the DinD approach used to assess

the impact of a microcredit programme on household consumption and income were also discussed. These estimation strategies were proposed to obtain unbiased and consistent

estimators, depending on the types of dependent variables as well as the nature of the dataset.

Chapter 5 supplements Chapters 2, 6 and 7. The description of the survey data and respondents provides an overview of the data obtained and used in the analysis of credit accessibility in chapter 6 and impact evaluation of cross sectional data in Chapter 7. Descriptive statistics provide variations and preliminary relationships of the explanatory variables in the models. Chapter 5 also provides an overview of how the rural credit market actually operates in the MRD. Particularly, the formal and informal lending were analysed based on individual, household, microcredit, and geographic factors. The descriptive analysis reflects the rural credit market in the MRD.

The research findings were obtained from Chapters 6 and 7 where accessibility to microcredit, amount of formal microcredit, amount of informal microcredit, and impact evaluation of microcredit programmes were investigated. Different estimation strategies were used in the

183 analysis. First, to obtain the determinants of informal microcredit, credit accessibility to formal microcredit, and formal microcredit, the conditional mixed process was applied. We used the conditional mixed process method to mimic the estimation strategies of the probit RHS, Tobit and Heckman 2step models in which the probit RHS and Tobit strategies were used to account for truncated and endogenous problems of informal microcredit in the accessibility to formal microcredit and the Heckman 2step strategies were applied to account for sample selection bias in the formal microcredit. Secondly, to obtain the impact estimators,

the PSM method and DinD approach were chosen to counter with observed and unobserved

bias. Under the condition that bias is controlled for using the observed covariates and pre- programme attributes, Kernel and Radius Matching were applied to estimate the impact of the microcredit programme on rural households using cross sectional data. To relax the

assumption that allows both observed and unobserved factors to bias the impact estimators of the microcredit programme, the IV-FE model was applies on the panel dataset. Before the IV- FE was estimated, the PSM was applied to remove bias possibly due to data issue (i.e., post- programme implementation data). The findings from this study are summarised in the next section.